Tap proximity

ABSTRACT

A system that identifies whether an object is within close proximity to a touch screen of a user device using one or more sensors. The system may optimize incentives based on what is being displayed on the user device when the system identifies the object.

TECHNICAL FIELD

The present disclosure generally relates to detecting objects withincertain proximity of a touchscreen and using the detection data as partof an incentive determination.

BACKGROUND

With traditional shopping at a merchant store, a shopper's interest invarious items can be determined by a store employee seeing what theshopping is looking at, trying on, or asking about. However, with theInternet and online shopping, this type of valuable information can behard to obtain. For example, online users typically tap or otherwiseselect content on a display screen to indicate interest, which candirect the user to additional information or start a purchasing flow. Itis difficult to determine whether the user is or was interested in anitem not selected. One or more of the embodiments disclosed below helpaddress this difficulty along with providing other advantages.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 is a block diagram of an exemplary computing system that isadapted for implementing a system for identifying non-touch based userinteractions with a touchscreen device and implementing consumerpredictions based on the identifications.

FIG. 2 is a block diagram of an exemplary computer system suitable forimplementing one or more devices of the computing system in FIG. 1.

FIG. 3 is a flow diagram illustrating an exemplary process of a systemproviding incentives based on non-touch bases user interactions with atouchscreen.

FIG. 4 is a flow diagram illustrating an exemplary system foridentifying non-touch based touchscreen interactions.

FIG. 5 is an exemplary incentive optimization process.

FIG. 6 is an exemplary user device capable of identifying non-touchbased user interactions with a touchscreen.

FIG. 7 depicts a sample tap proximity situation that one or more systemsmay handle.

Embodiments of the present disclosure and their advantages are bestunderstood by referring to the detailed description that follows. Itshould be appreciated that like reference numerals are used to identifylike elements illustrated in one or more of the figures, whereinshowings therein are for purposes of illustrating embodiments of thepresent disclosure and not for purposes of limiting the same.

DETAILED DESCRIPTION

In the following description, specific details are set forth describingsome embodiments consistent with the present disclosure. It will beapparent, however, to one skilled in the art that some embodiments maybe practiced without some or all of these specific details. The specificembodiments disclosed herein are meant to be illustrative but notlimiting. One skilled in the art may realize other elements that,although not specifically described here, are within the scope and thespirit of this disclosure. In addition, to avoid unnecessary repetition,one or more features shown and described in association with oneembodiment may be incorporated into other embodiments unlessspecifically described otherwise or if the one or more features wouldmake an embodiment non-functional.

As technology has progressed and the internet has become more and moreprevalent, companies have been able to record large amounts consumerdata. However, developments for obtaining data related to touchscreeninteractions have been lacking. For example, determining whether a usercame close to tapping and/or actuating a touchscreen is valuableinformation. This type of information could be used to customize a userexperience and better market products and/or services. However, currentsystems do not allow for obtaining this information. This is likely dueto several technological hurdles that must be overcome to obtain suchinformation. One of the major hurdles is simply detecting whether anindividual is about to actuate a touchscreen. There is no simple datasensor to identify this situation. Unlike a cursor, which a system canaccurately pin point through interactions with a user device,touchscreens do not have a simple mechanism for determining user actionswhere the touchscreen is not being contacted.

It would be beneficial if a system could determine whether an object,such as a user finger, is in proximity with a touchscreen device. Such asystem would provide access to this consumer data for analysis.

Furthermore, it would be beneficial if there was a system that could usethe detected information to directly affect the user experienceindividually. Current systems often aggregate data from many users andprovide the data to merchants and/or service providers for analysis.Afterwards, it is up to the merchant to use the consumer intelligence tomake changes either to pricing, consumer interfaces, advertisementtargeting and/or the like. This is generally a change that all users seeand is not very individualized. Often, this is the result of the dataanalytics system not being able to directly interface with either theuser device and/or the merchant system. A system that would be able tointerface and exchange relevant data between merchant devices and userdevices to create an individualized experience is complicated andrequires a fairly intricate system. Such a system would need to haveconforming data types, data identifiers, and specifics applicationinterfaces.

However, a system that would be capable of orchestrating and interactingwith merchant systems would be beneficial. Such a system would allow formerchant and advertisement systems to create an individualizedexperience for a consumer based on consumer data.

Additionally, it would be desirable if the user experience could beadjusted based on the tap proximity determination data for the user. Forexample, a system that could predict how to customize user experiencessuch that that it resulted in an amicable agreement between the merchantand consumer would be useful.

It would also be beneficial if a system could provide better targetedadvertisements. Current systems basically use website browsinginformation to determine which advertisements to provide. However, suchmethods are poor at determining whether the advertisements are currentlyeffective and/or whether the individual was actually interested in theadvertised product. Furthermore, users generally browse many differentwebsites and objects on a daily basis. Because there is a limit on thenumber of advertisements that can be displayed at one time, systems haveto decide what to advertise. It would be beneficial if a system coulddetermine which advertisements are likely to be more successful or arecurrently successful. Additionally, it would be beneficial if theadvertisements could adjust or change in real time based on indicationsthat the user is interested in the product.

Furthermore, it would be desirable if the system could regularly adjustbased on verifiable predictions about a individual. The difficulty,often times, in making a prediction on a consumer preference based onconsumer data is that the prediction is not verifiable. For example,consumer data, such as consumer purchasing data, may indicate that aproduct with a specific feature is more desirable than without it. Theconsumer data may show that a product that is the color red has soldmore easily than the product in another color. At this point, themerchant can use this information to predict that the product with thecolor red could be sold for a higher price. However, this prediction isnot verifiable until the price is actually raised and consumers eitherbuy or do not buy the product at the new price. This presents a largerisk to the merchant. Furthermore, the merchant must make a predictionon how much the price should be raised based on the consumer data.Pricing the sale of a service and or product requires a careful balancebetween sale volume and margins. A price too high may increase marginsbut overly reduce sales, and thus reducing profitability. On the otherhand reducing the price too much may increase sales but at too low of amargin, and therefore also reducing profitability. Determining how pricechanges are affecting profitability and sales is a slow process and alot of money can be lost when experimenting with different prices.Instead, it would be better if a system could iteratively makepredictions based on feedback and continuously optimize its predictionsin a quick adaptive manner.

One or more of the embodiments disclosed below addresses the desirablefeatures discussed above in addition to other desirable features.

Merchants, service providers, and advertisers are all interested indetermining whether an individual is interest in a good and/or service.Merchants and service provides are particularly interested indetermining why users purchase or do not purchase goods or service, thetype of consumers that buy or do not buy a product or service, and howinterested a user is in a product or service.

For example, merchants and service providers would be interested inknowing whether a decision to purchase goods or services was instant ortook a bit of thought. This information may be used to determine howinterested a person was in the good or service. In some examples, amerchant and or service provider may be able to use such information tohelp increase the odds of a sale.

In a similar manner, such information could help advertisers.Advertisers may be able to determine demographics and/or users thatadvertisements for a product or service work best on. In this manner,advertisers may be able to target advertisements to increase the chancesof the advertisement leading to a purchase. Some advertisers may getpaid by the number of unique users who click on an advertisement, andbecause there is a limit on the number of advertisements a user can see,it would be advantageous to customize advertisements that the user wouldbe most interested in.

In some examples, a system and method for determining user interest isprovided. In some embodiments, the interest is determined based on theproximity of a tapping device, such as a finger, stylus, and/or thelike, to specific content on a display. In some embodiments, the userinterest may be determined based on movement and/or the location of avirtual pointer, such as a mouse pointer/arrow. In some examples asystem may determine when a user almost tapped and/or actually tapped onan advertisement, product, and/or a specific image on the touchscreen.In some examples the system may monitor tap data, such as how long atapping device hovered over the touchscreen before tapping, and whetherone or more stimuli that may have contributed to causing the tap or not.In some examples, the system may monitor the location of a mouse andclick data of the mouse rather than tap data.

In some examples, the system may monitor one or more sensors, such as afront facing camera, heat sensor, pressure sensor, and/or the like tomonitor touch based activity on a user device. In some examples,whenever a tap on the user device is received, the system may recordsensor data from the user device just before the tap occurred formachine learning purposes. The system may use the recorded data todetermine or predict when a user is about to tap on the touchscreen. Insome examples, the system may monitor sensor data and compare thereceived sensor data with the recorded sensor data to determine whethera user almost tapped on or came in close proximity to a touchscreen.

In some examples, the system may monitor whether a user almost tapped ascreen for a purchase checkout, advertisement, and/or the like. Thisdata may be provided to the product, service, and/or advertisementprovider for implementing one or more actions. For example, a product,service, and/or advertisement provider may request that the systemprovide an incentive, start a chat window, and/or provide a number tocall to a user in response to receiving the almost tap indication.

In some examples, the product, service, and/or advertisement providermay be able to target the actions to a certain demographic. For example,the actions may be targeted to users who are located in a certain area,such as a city, state, country, and/or the like. The location may bedetermined from a global positioning system, user information, and/orthe like. In some examples, the targeting may be dependent on time ofday.

In some examples, the system may also monitor facial expressions forwhen a user almost tapped a screen. The system may use a front facingcamera to take images of the face of a user. The system may analyze thefacial image for certain expressions or emotions. The system maydetermine a pattern of facial expressions that are associated with analmost tap, and in response, display target advertisements for a productand/or service for when the user has a different facial expression.

In some examples, the system may monitor facial expressions for aparticular product but with different features and or attributes, suchas the color of the product. The system may attempt to determine apattern of facial expressions for when a purchase for a product with acertain color occurs and target advertisements for the product with thecertain color based on the facial expression of the user.

In some examples, the system may identify when a user is about to tap ascreen for a product and/or advertisement, and in response, contact aproduct provider and/or advertisement provider to review the user anddecide whether an incentive should be provided. For example, when a useris hovering their finger over a purchase button, the system may alert amerchant that a user is about to purchase a product but their actionsindicate that the user is hesitant. The system may provide informationabout the user such that the provider can determine the best incentivefor overcoming the hesitation. This information may indicate how longthe user has been viewing the product, how many times the user hasviewed the product, whether there are any products associated with theproduct being viewed, the price of the product, and/or the like. Forexample, a user may be displaying hesitancy in purchasing a camera, andthe user has been viewing the product for longer than five minutes. Thesystem may provide this information to the merchant and provide someoptional incentives to get the user to purchase the camera, such asgiving a free camera bag, camera lens, camera strap, or a money discountto the purchaser.

To maintain the highest possible sales margin, it is in the interest ofthe merchant to provide an incentive that is just enough to get the userto purchase a product. In this manner, the merchant may be able tomaximize their profits. In some examples, the system may suggest orimplement incentives based on the user information. The system may thendetermine whether the incentive was successful or not and adjust futureincentives accordingly.

However, determining the threshold incentive can be difficult for acomputer. Humans, on the other hand, may be better at making predictiveand intuitive estimates when enough information is provided. As such thesystem may to provide the user information to the merchant. In someexamples, the information may be provided in a format that is easilyingestible, such as trends associated with the user information. In thismanner, a human associated with the merchant may be able to make ajudgment on how big of a discount should be provided to the user. Suchinformation may include identifying users who are displaying hesitancy,information about the user (e.g. purchasing habits, prior purchases fromthe merchant, how long the finger of the user is hovering over apurchase button, and/or the like), information about past incentivesthat were successful and/or unsuccessful in pushing other users topurchase a product, and/or the like.

The system may provide an intuitive graphical user interface to themerchant such that the merchant can quickly and easily provide anincentive to the user as the user is browsing a product. Furthermore,the system may discretely provide the incentive to the user such thatthe user may believe that the discount was serendipitous. For example,the system may email or text the user with a coupon while the user isbrowsing the product. The coupon may be a general percentage discountwith no indication of the product the user is browsing. In this mannerthe user may believe this discount just happened to come along at thesame time as the user was browsing the product, and use the discount topurchase the product the user was hesitant about.

FIG. 1 illustrates, in block diagram format, an exemplary embodiment ofa computing system adapted for implementing a system for identifyingnon-touch based user interactions with a touch screen device andimplementing consumer predictions based on the identification. As shown,a computing system 100 may comprise or implement a plurality of serversand/or software components that operate to perform various methodologiesin accordance with the described embodiments. Exemplary servers mayinclude, for example, stand-alone and enterprise-class servers operatinga server operating system (OS) such as a MICROSOFT® OS, a UNIX® OS, aLINUX® OS, or other suitable server-based OS. It may be appreciated thatthe servers illustrated in FIG. 1 may be deployed in other ways and thatthe operations performed and/or the services provided by such serversmay be combined, distributed, and/or separated for a givenimplementation and may be performed by a greater number or fewer numberof servers. One or more servers may be operated and/or maintained by thesame or different entities.

Computing system 100 may include, among various devices, servers,databases and other elements, one or more clients 102 that may compriseor employ one or more client devices 104, such as a laptop, a mobilecomputing device, a tablet, a PC, a wearable device, and/or any othercomputing device having computing and/or communications capabilities inaccordance with the described embodiments. Client devices 104 mayinclude a cellular telephone, smart phone, or other similar mobiledevices that a user may carry on or about his or her person and accessreadily.

Client devices 104 generally may provide one or more client programs106, such as system programs and application programs to perform variouscomputing and/or communications operations. Exemplary system programsmay include, without limitation, an operating system (e.g., MICROSOFT®OS, UNIX® OS, LINUX® OS, Symbian OS™, Embedix OS, Binary Run-timeEnvironment for Wireless (BREW) OS, JavaOS, a Wireless ApplicationProtocol (WAP) OS, and others), device drivers, programming tools,utility programs, software libraries, application programming interfaces(APIs), and so forth. Exemplary application programs may include,without limitation, a web browser application, messaging applications(e.g., e-mail, IM, SMS, MMS, telephone, voicemail, VoIP, videomessaging, internet relay chat (IRC)), contacts application, calendarapplication, electronic document application, database application,media application (e.g., music, video, television), location-basedservices (LBS) applications (e.g., GPS, mapping, directions, positioningsystems, geolocation, point-of-interest, locator) that may utilizehardware components such as an antenna, and so forth. One or more ofclient programs 106 may display various graphical user interfaces (GUIs)to present information to and/or receive information from one or moreusers of client devices 104. In some embodiments, client programs 106may include one or more applications configured to conduct some or allof the functionalities and/or processes discussed below.

As shown, client devices 104 may be communicatively coupled via one ormore networks 108 to a network-based system 110. Network-based system110 may be structured, arranged, and/or configured to allow client 102to establish one or more communications sessions between network-basedsystem 110 and various computing devices 104 and/or client programs 106.Accordingly, a communications session between client devices 104 andnetwork-based system 110 may involve the unidirectional and/orbidirectional exchange of information and may occur over one or moretypes of networks 108 depending on the mode of communication. While theembodiment of FIG. 1 illustrates a computing system 100 deployed in aclient-server operating environment, it is to be understood that othersuitable operating environments and/or architectures may be used inaccordance with the described embodiments.

Data communications between client devices 104 and the network-basedsystem 110 may be sent and received over one or more networks 108 suchas the Internet, a WAN, a WWAN, a WLAN, a mobile telephone network, alandline telephone network, as well as other suitable networks. Forexample, client devices 104 may communicate with network-based system110 over the Internet or other suitable WAN by sending and or receivinginformation via interaction with a web site, e-mail, IM session, and/orvideo messaging session. Any of a wide variety of suitable communicationtypes between client devices 104 and system 110 may take place, as willbe readily appreciated. In particular, wireless communications of anysuitable form may take place between client device 104 and system 110,such as that which often occurs in the case of mobile phones or otherpersonal and/or mobile devices.

In various embodiments, computing system 100 may include, among otherelements, a third party 112, which may comprise or employ third-partyservers 114 hosting third-party applications 116. In variousimplementations, third-party servers 114 and/or third-party applications116 may host applications associated with or employed by a third party112. For example, third-party servers 114 and/or third-partyapplications 116 may enable network-based system 110 to provide client102 with additional services and/or information, such as data logging,data communications, security functions, targeted advertising, customersupport, and/or other services, some of which will be discussed ingreater detail below. Third-party servers 114 and/or third-partyapplications 116 may also provide system 110 and/or client 102 withother information and/or services, such as email services and/orinformation, property transfer and/or handling, purchase services and/orinformation, and/or other online services and/or information.

In one embodiment, third-party servers 114 may include an email serverthat hosts a user's email account. In some embodiments, the third-partyservers may include advertisement selection server for providingadvertisements. In yet another embodiment, third-party severs 114 mayinclude one or more servers for aggregating user data and statistics.

Network-based system 110 may comprise one or more communications servers120 to provide suitable interfaces that enable communication usingvarious modes of communication and/or via one or more networks 108.Communications servers 120 may include a web server 122, an API server124, and/or a messaging server 126 to provide interfaces to one or moreapplication servers 130. Application servers 130 of network-based system110 may be structured, arranged, and/or configured to provide variousonline services, account management, parameter management, parametermonitoring, parameter execution, monetary transfers, device management,device monitoring, data gathering, data analysis, and other services tousers that access network-based system 110. In various embodiments,client devices 104 and/or third-party servers 114 may communicate withapplications servers 130 of network-based system 110 via one or more ofa web interface provided by web server 122, a programmatic interfaceprovided by API server 124, and/or a messaging interface provided bymessaging server 126. It may be appreciated that web server 122, APIserver 124, and messaging server 126 may be structured, arranged, and/orconfigured to communicate with various types of client devices 104,third-party servers 114, third-party applications 116, and/or clientprograms 106 and may interoperate with each other in someimplementations.

Web server 122 may be arranged to communicate with web clients and/orapplications such as a web browser, web browser toolbar, desktop widget,mobile widget, web-based application, web-based interpreter, virtualmachine, and so forth. API server 124 may be arranged to communicatewith various client programs 106 and/or a third-party application 116comprising an implementation of API for network-based system 110.Messaging server 126 may be arranged to communicate with variousmessaging clients and/or applications such as e-mail, IM, SMS, MMS,telephone, VoIP, video messaging, IRC, and so forth, and messagingserver 126 may provide a messaging interface to enable access by client102 and/or third party 112 to the various services and functionsprovided by application servers 130.

Application servers 130 of network-based system 110 may be a server thatprovides various user device monitoring, advertisement management, datagathering, and discount management services. Application server 130 ofnetwork-based system 110 may provide services such as, account services,authentication services, product management services, payment services,user data gathering services, location services, data analysis services,notification services, fund transfer, funds and/or currency exchanges,and/or other services. Application servers 130 may include an accountserver 132, a device monitoring server 138, a task incentive managementserver 140, an advertisement server 142, a notification server 144,and/or a message server 146. Application servers 130 may further includea device identification server 134, a tap prediction server 136, and/ora messaging server 146. These servers, which may be in addition to otherservers, may be structured and arranged to monitor user devices andpredict user hesitancy on a touch screen device in addition to some orall of the other services as discussed above and in more detail below.

Application servers 130, in turn, may be coupled to and capable ofaccessing one or more databases 150 including an incentive database 152,a user account database 154, and/or training database 156. Databases 150generally may store and maintain various types of information for use byapplication servers 130 and may comprise or be implemented by varioustypes of computer storage devices (e.g., servers, memory) and/ordatabase structures (e.g., relational, object-oriented, hierarchical,dimensional, network) in accordance with the described embodiments.

FIG. 2 illustrates an exemplary computer system 200 in block diagramformat suitable for implementing on one or more devices of the computingsystem in FIG. 1. In various implementations, a device that includescomputer system 200 may comprise a personal computing device (e.g., asmart or mobile phone, a computing tablet, a personal computer, laptop,wearable device, PDA, etc.) that is capable of communicating with anetwork. A service provider and/or a content provider may utilize anetwork computing device (e.g., a network server) capable ofcommunicating with the network. It should be appreciated that each ofthe devices utilized by users, service providers, and content providersmay be implemented as computer system 200 in a manner as follows.

Additionally, as more and more devices become communication capable,such as new smart devices using wireless communication to report, track,message, relay information and so forth, these devices may be part ofcomputer system 200. For example, windows, walls, and other objects maydouble as touch screen devices for users to interact with. Such devicesmay be incorporated with the systems discussed herein.

Computer system 200 may include a bus 202 or other communicationmechanisms for communicating information data, signals, and informationbetween various components of computer system 200. Components include aninput/output (I/O) component 204 that processes a user action, such asselecting keys from a keypad/keyboard, selecting one or more buttons,links, actuatable elements, etc., and sends a corresponding signal tobus 202. I/O component 204 may also include an output component, such asa display 211 and a cursor control 213 (such as a keyboard, keypad,mouse, touchscreen, etc.). An optional audio input/output component 205may also be included to allow a user to use voice for inputtinginformation by converting audio signals. Audio I/O component 205 mayallow the user to hear audio. A transceiver or network interface 206transmits and receives signals between computer system 200 and otherdevices, such as another user device, a merchant server, an emailserver, application service provider, web server, a payment providerserver, and/or other servers via a network. In various embodiments, suchas for many cellular telephone and other mobile device embodiments, thistransmission may be wireless, although other transmission mediums andmethods may also be suitable. A processor 212, which may be amicro-controller, digital signal processor (DSP), or other processingcomponent, processes these various signals, such as for display oncomputer system 200 or transmission to other devices over a network 260via a communication link 218. Again, communication link 218 may be awireless communication in some embodiments. Processor 212 may alsocontrol transmission of information, such as cookies, IP addresses,and/or the like to other devices.

Components of computer system 200 also include a system memory component214 (e.g., RAM), a static storage component 216 (e.g., ROM), and/or adisk drive 217. Computer system 200 performs specific operations byprocessor 212 and other components by executing one or more sequences ofinstructions contained in system memory component 214. Logic may beencoded in a computer readable medium, which may refer to any mediumthat participates in providing instructions to processor 212 forexecution. Such a medium may take many forms, including but not limitedto, non-volatile media, volatile media, and/or transmission media. Invarious implementations, non-volatile media includes optical or magneticdisks, volatile media includes dynamic memory, such as system memorycomponent 214, and transmission media includes coaxial cables, copperwire, and fiber optics, including wires that comprise bus 202. In oneembodiment, the logic is encoded in a non-transitory machine-readablemedium. In one example, transmission media may take the form of acousticor light waves, such as those generated during radio wave, optical, andinfrared data communications.

Some common forms of computer readable media include, for example,floppy disk, flexible disk, hard disk, magnetic tape, any other magneticmedium, CD-ROM, any other optical medium, punch cards, paper tape, anyother physical medium with patterns of holes, RAM, PROM, EPROM,FLASH-EPROM, any other memory chip or cartridge, or any other mediumfrom which a computer is adapted to read.

In various embodiments of the present disclosure, execution ofinstruction sequences to practice the present disclosure may beperformed by computer system 200. In various other embodiments of thepresent disclosure, a plurality of computer systems 200 coupled bycommunication link 218 to the network (e.g., such as a LAN, WLAN, PTSN,and/or various other wired or wireless networks, includingtelecommunications, mobile, and cellular phone networks) may performinstruction sequences to practice the present disclosure in coordinationwith one another. Modules described herein may be embodied in one ormore computer readable media or be in communication with one or moreprocessors to execute or process the steps described herein.

A computer system may transmit and receive messages, data, informationand instructions, including one or more programs (i.e., applicationcode) through a communication link and a communication interface.Received program code may be executed by a processor as received and/orstored in a disk drive component or some other non-volatile storagecomponent for execution.

Where applicable, various embodiments provided by the present disclosuremay be implemented using hardware, software, or combinations of hardwareand software. Also, where applicable, the various hardware componentsand/or software components set forth herein may be combined intocomposite components comprising software, hardware, and/or both withoutdeparting from the spirit of the present disclosure. Where applicable,the various hardware components and/or software components set forthherein may be separated into sub-components comprising software,hardware, or both without departing from the scope of the presentdisclosure. In addition, where applicable, it is contemplated thatsoftware components may be implemented as hardware components andvice-versa.

Software, in accordance with the present disclosure, such as programcode and/or data, may be stored on one or more computer readable media.It is also contemplated that software identified herein may beimplemented using one or more computers and/or computer systems,networked and/or otherwise. Such software may be stored and/or used atone or more locations along or throughout the system, at client 102,network-based system 110, or both. Where applicable, the ordering ofvarious steps described herein may be changed, combined into compositesteps, and/or separated into sub-steps to provide features describedherein.

The foregoing networks, systems, devices, and numerous variationsthereof may be used to implement one or more services, such as theservices discussed above and in more detail below.

FIG. 3 illustrates an exemplary process 300 that may be implemented by asystem to detect hesitancy to tap a touch screen device. Process 300 maybe implemented on a system such as system 100 of FIG. 1 according tosome embodiments. According to some embodiments, process 300 may includeone or more of operations 301-306 which may be implemented, at least inpart, in the form of executable code stored on a non-transitory,tangible, machine readable media that, when run on one or moreprocessors, may cause a system to perform one or more of the operations301-306.

At operation 301, the system may receive sensor information from a userdevice for detecting whether an object is about to tap on a touchscreenof the user device. The manner in which the sensor data is analyzed maydepend on the type of sensor data that the system receives.

In some examples, the sensor data may be from one or more proximitysensors, such as a capacitive sensor, photoelectric sensor, and/or thelike. The proximity sensor may be configured to sense objects that arewithin a threshold distance of a touchscreen of the device. For examplethe proximity sensor may be configured to detect object within athreshold distance, such as 5 mm. In some examples, the proximity sensormay be an attachment to the user device that communicatively coupleswith the system. In some examples, the sensor may communicatively couplewith the system through the user device.

In some examples, the sensor data may be from one or more image sensors,such as one or more of semiconductor charge-coupled devices (CCD),active pixel sensors of a metal-oxide-semiconductor (CMOS), active pixelsensors of a N-type metal-oxide-semiconductor (NMOS), and/or the like.When sensor data of an image sensor is received, the data may be in theform of one or more arrays and/or matrices. The cells of the arraysand/or matrices may represent a color and/or intensity for a pixel of animage.

In some examples, the sensor data may be a heat map sensed by thedevice. For example, the front of a user device may have one or moreheat sensors. In some examples a plurality of heat sensors forming amatrix on the user device may monitor heat intensities. The heat sensordata may also be provided in the form of a matrix, the cells of thematrix having values indicating heat intensity on certain locations ofthe heat sensor. In some examples, the heat intensities sensed on theheat sensor may correspond to a location of a touch screen of the userdevice.

In some examples, the sensor data may be from an infrared sensor,thermographic camera, and/or infrared camera. The sensor information maybe data provided in an array and/or matrices. The cells of the arraysand/or matrices may represent a color and/or intensity for a pixel of athermographic image. The different colors of the pixels and/or intensitymay represent and/or correspond to a heat value of a heat color map. Thesystem may be able to determine a heat value based on the color andintensity of each pixel. In some examples, the sensor may be anaccessory or peripheral device that connects to the user device andprovides the sensor information. For example, the sensor may be a cameraattachment, a case, screen protector, and/or the like.

In some examples, the system may combine information from multiplesensors to create a combined sensor reading image and/or data file. Forexample, an infrared camera sensor may provide thermal imaging datawhich may be combined with imaging data from a CMOS sensor. In thismanner, the image from the CMOS sensor may provide an outline or contextto the objects that are providing the heat readings to the infraredcamera. In some examples, the system may create an outline of image datafrom the CMOS sensor using various edge detection algorithms andsuperimpose that image with the heat image. In this manner, the outlinesmay delineate different objects that are producing each heat response.

In some examples, the sensor data may include identifiers that indicatethe type of data being received and/or data format of the sensor data.In this manner, the system may be able to differentiate different sensordata for analysis and/or determine which data types to combine beforeanalysis.

In instances where there are multiple sensor and/or power hungrysensors, it may be useful to have one or more energy saving techniqueson the user device to increase battery life. Having all of these sensorsturned on in a constant monitoring mode may be an inefficient use ofenergy. As such, in some examples, one or more of the energy savingtechniques may be implemented on the user device such that the energyused by the sensors may be reduced. In some examples, the energy savingsystem and sensor monitoring system may be one or more applications on auser device that communicates and couples with the system. In someexamples, to reduce energy consumption, the sensors on the user devicemay remain off until a certain condition is met. Some exemplaryconditions may include instances when the device unlocks a lock screen,the device is using a particular application, the device is using a webbrowser, the device is displaying a certain particular advertisement,the display of the device is turned on, and/or the like.

In some examples, the device may turn on a sensor or monitor a fractionof the pixels on a sensor until a condition occurs rather than havingthe entire sensor turned on. In response to the condition occurring, thesystem may then turn on all of the pixels of the sensor. In someexamples, when there are multiple sensors, one or more of the sensorsmay be turned off until a condition occurs on one or more other sensors.

Some exemplary sensor based conditions may include a sensor reading ofheat above a certain threshold value or between two thresholds. In someexamples, the condition may be when a heat sensor reads a large heatdifference between readings or threshold period of time. In someexamples, the condition may be a heat reading pattern where there is asharp difference and/or threshold difference in heat readings betweenpixels. In some examples, the condition may be when a heat readingpattern and/or signature is conical or cylindrical in shape, which maybe based on the heat pattern a finger and hand may create. In someexamples, the condition may be a sharp light intensity change on aportion of the pixels.

At operation 302, the system may receive information about the device,device usage information, device status information, and/or the like. Insome examples, the system may receive information about the device, suchas a device identifier. Some examples of device identifiers include, butare not limited to, unique device identifier (UDID), Android ID,international mobile equipment identity (IMEI), international mobilesubscriber identity (IMSI), an assigned mobile number, and/or the like.The system may use the device identifier for categorizing, storing,cataloging, recording, identifying, associating, and/or retrieving dataassociated with the device.

The system may also receive device status information. Device statusinformation may include internet activity, application activity, and/orthe like. In some examples, device status information may includeinformation such as what advertisements have been displayed, what isbeing displayed on the user device, what has been displayed within apredetermined amount of time on the user device, website browsinghistory, product purchases that were made through an application, howlong a product was browsed on an application, what products weredisplayed on the device, what products are currently displayed on thedevice, if there is a product in a checkout portion of an application orwebsite, and/or the like.

In some examples, the system may log and record the received data in afolder or in a data partition associated with the device identifier oran account associated with the device identifier.

In some examples, the system may also receive other information, such asreadings from a global position system (GPS), time of day, weather,applications that are installed on the device, user accounts associatedwith the device, demographic information (e.g., age, weight, fitnesslevel, height, and/or the like).

At operation 303, the system may determine from the data received atoperation 302 what products, services, and/or advertisements are beingbrowsed or on display of the user device. For example, a user may be ata product purchase page for a product or a product may be currentlyadvertised in a side bar of a webpage. In some examples, the system maydetermine that the user device is at a checkout page right before thepurchase of a product. The system may also check in the database forwhether the product being displayed had been displayed on the device ata previous time, and if so, for how long. The system may also determinehow long the product has been on display on the user device.

In some examples, the products may be determined from metadata of animage of the product or provided by the application used to view theproduct. In some examples, advertisements, product pages, checkoutpages, and/or applications may provide identifying information about theproduct. Some exemplary identifiers may be the name of the product, auniversal product identifier, stock keeping unit SKU, and/or anotheridentifier for the product.

At operation 304, the system may determine whether an object such as afinger, hand, stylus, or other object is in close proximity to atouchscreen of the user device. This may be an attempt to identify thata user is about to purchase a product but is displaying some hesitancy.In some examples, close proximity may be detectable distance of asensor, such as a detection range of a proximity sensor. In someexamples, the system may simply determine, based on sensor informationreceived at operation 301, whether an object, such as a finger, isdetected by the sensor. In some example, the system may determinewhether an object is within close proximity based on whether the sensorinformation indicates that the object can be detected within apredetermined resolution. For example, the system may determine theobject is within close proximity when a certain number of pixels of animage sensor detect the object. In some examples, the system may receiveimage information, and the system may determine whether the objectencompasses a threshold percentage and/or number of the image pixels. Insome examples, the system may use a training database to predict whetherand object is in close proximity to a screen as discussed in more detailbelow.

In some examples, the system may also determine how long the object hasstayed continuously in close proximity to the screen, similar to someonewho may be hesitant to complete and action. In some examples, the systemmay continue to receive sensor information at operation 301 and continueto determine that an object is close to the screen in operation 304. Thesystem may monitor the sensor information continuously being received atoperation 301 until the system determine that there is not and objectclose to the screen. The system may then determine the time differencein time stamps from when the system first detected the time stamp towhen the system no longer detected the object.

At operation 305, the system may determine whether any incentives areavailable for the product determined at process 303. The system maysearch a database of incentives to see if one matches the productidentifier determined at operation 303. In some examples, the incentivesmay be associated with one or more rules or conditions for providing theincentive. In some examples, the rules may be provided and/or set by amerchant through a third-party device. For example, a rule may belimited to a geographic location. In such an example, the system may usethe location information received at operation 302 to determine whetherthe device is within the geographic location and qualifies for theincentive. Other possible rules and/or conditions may be based ondemographic information (such as age, gender, and/or other demographicinformation), a set limit on the number of incentives provided for theproduct, a limit on the number of incentives for a device or an account,number of full priced items purchased with a device or an account,number of purchases made from one or more brands and/or merchants usinga device or account, whether a purchase was made from a brand ormerchant within a predetermined time, how long the object was detectedat operation 304, and/or the like.

Some exemplary incentives may be a monetary discount on the product orservice, an additional free product and/or service, a discount on arelated product and/or service, free warranty, and/or the like.

In some embodiments, the system may use the information received to aidin advertisement targeting. As discussed in operation 304, the systemmay determine that an object was close to a touch screen while a productwas on display. The system may determine that an object was close to thescreen for a prolonged period of time when a product was on display. Thesystem may determine that this is an indicator that the user isinterested in the product. For example, the system may determine fromthe above discussed time stamps that an object was in close proximitywith the touch screen for more than a threshold period of time, such as5 seconds. However, the user may not have made the purchase and decidedto move on or purchase something else. The system may record thisinformation and provide it to an advertisement placement server forhelping the server decide which advertisements to place. Theadvertisement server may be configured to choose a rotation ofadvertisements to a user based on browsing history, but to displayadvertisements more frequently when it receives an indication from thesystem that the user showed interest in a particular product.

When the system determines that an incentive is available for theproduct and the rules and conditions for all the invectives are met, thesystem may offer the incentive at operation 306. In some examples, themanner in which the incentive is provided may be determined by a thirdparty. A third party, through a third-party device, may indicate thatthe offer be provided through a message, such as short messagingservice, email, instant message, and/or the like. There may be severaldifferent manners in which the incentives are provided. For example, apop-up window with a coupon may be displayed. In some examples, theprice on the page may be slashed directly on the page without a pop-upwindow. The page may provide a message such as “please accept thisdiscount as a loyal customer.” Other examples of incentives may includethe addition of a free product, fee shipping, free upgraded shipping,free upgrades, and/or the like.

In some examples, to orchestrate providing the incentive, the system maybe in communication with another server or system that is handling thedisplay of the product. For example, the user may be communicating witha content host system, such as a merchant system or merchant serverhosting the website or content that the user is viewing. The system maythen indicate to the content host system to reduce prices and/or provideanother incentive on one or more products being sold to the user. Forexample, the system may provide the device identifier and productidentifier that the user is viewing to the content host system, and thecontent host system may, in response, reduce the price and/or provideanother incentive for the product identified by the product identifierwhen being viewed by a device with the device identifier.

In some examples, the system may contact the product seller forauthorization to provide the incentive. In some examples, the system mayprovide the product seller contact information such that the seller candirectly contact the user of the user device to provide an incentive.

FIG. 4 illustrates an exemplary process 400 that may be implemented by atap prediction system. Process 400 may be implemented on a system suchas system 100 of FIG. 1 according to some embodiments. According to someembodiments, process 400 may include one or more of operations 401-406which may be implemented, at least in part, in the form of executablecode stored on a non-transitory, tangible, machine readable media that,when run on one or more processors, may cause a system to perform one ormore of the operations 401-406.

At operation 401, the system may receive sensor information from one ormore user devices for creating a training base. The system may updateand use the training database for predicting whether an object is closeto and/or about to tap a touchscreen of the user device. In someexamples, the sensor information may be from one or more sensors on theuser device similar to the sensors discussed in operation 301 of FIG. 3.

In some examples, the system may maintain multiple different trainingdatabases based on one or more factors. For example, the system maycreate a different training database for different device categoriesand/or models. Different device models and categories may have differentplacements of sensors and different types of sensors such that the datareceived from one device may not be a good training data point foranother device. Furthermore, in some examples, the sensor informationmay come from an attachment or an adapter, and as such, a differentdatabase may be created based on adapter information.

In some examples, the system may create a different training databasefor each device unique identifier. User mannerisms, hand sizes, fingersizes, and tapping techniques may differ from individual to individual.As such, having an individualized training database for each deviceidentifier may allow the system to make more accurate in detectingobjects because user devices are usually used by a single owner.

At operation 402, the system may receive an indication from the userdevice that the touchscreen received an input, such as a finger or otherobject touching the touchscreen. In order for the system to accuratelypredict whether an object is within tapping range of the touchscreen,the system may create a training database with confirmed taps. Becausean object tapping the touchscreen also has to come close to thetouchscreen before the tap occurs, the system can use the sensorinformation received just before a tap occurred as training data topredict future taps and/or whether an object is within tapping range ofthe touchscreen. In this manner, the system may use the tap indicationreceived at operation 402 as a trigger to record the sensor informationjust before the tap occurred as a verified data point of an object beingwithin tap or close proximity of the touchscreen.

At operation 403, the system, in response to receiving the tapindication at operation 402, may record the sensor information receivedat operation 401 just before the receipt of the tap indication atoperation 402. In some examples, the system may receive device sensorinformation at short and regular intervals from the user device atoperation 401, and the system may maintain a buffer and/or cash of thesensor information. In some examples, the intervals may be at apredetermined periodicity. In some examples, the intervals may depend onthe device. In some examples, the intervals may be based on time and/orclock cycles.

In some examples, the system may maintain a buffer of sensor readings.The buffer may be a predetermined amount. The amount may be based on amemory size, number of readings, and/or time interval. In some examples,the system may transfer the buffered sensor readings into the trainingdatabase. In some examples, the system may take a subset of the bufferedsensor readings into the training database. For example, the system mayrecord one or more sensor readings that occurred just before the tapindication. In some examples, the system may receive timestampinformation along with the sensor readings and the tap indication, andthe system may use the time stamp information to determine which sensorreadings to record. In some examples, the system may record sensorreadings that have time stamps within a predetermined interval at apredetermined amount of time before the time stamp of the tapindication.

The sensor information may be held in a training database such that thesystem can predict whether a tap is about to occur or whether there isan object in a position to tap the touchscreen of the user device.

In some examples, the system may also record some sensor data in thetraining database that did not result in a tap indication within apredetermined amount of time. In this manner, the system may have twoclasses of data in the training database, one class with a positive tapindication and another without.

At operation 404, the system may receive sensor data from one or moreuser devices, much like the data received at operation 401. However, thesensor data received at operation 404 may be from a differentapplication or may be received from another application interface withthe system such that the system is requested to categorize the sensorinformation or determine whether and object is in close proximity withthe touchscreen. In some examples, the sensor data may be received witha specific request.

At operation 405, the sensor data may predict whether an object is inclose proximity with the touch screen by classifying the sensor datareceived at operation 404 using the training database at operation 403.The classification may be a binary prediction of whether the sensor datapredicts an object in close proximity with the touch screen or not. Insome examples, the system may calculate a mean for the trainingdatabase. The system may determine that received sensor data located ata predetermined number of standard deviations away from the mean as nothaving an object near the touchscreen. Additionally, the system maypositively predict that an object is close to the touch screen when thesensor data is located within the predetermine number of standarddeviations of the mean.

In some examples, the system may use one or more supervised learningalgorithms with the training database to predict whether an object iswithin tap proximity or close to the touchscreen of the device. Someexemplary supervised learning algorithms may include decision trees,linear regression, Naïve Bayesian classifier, neural networks, and/orthe like.

At operation 406, the classification may be used to predict whether anobject is in close proximity to the device or in a position to tap thetouchscreen based on how the data received at operation 404 isclassifies using the training database.

FIG. 5 illustrates an exemplary process 500 that may be implemented tooptimize incentives. Process 500 may be implemented on a system such assystem 100 of FIG. 1 according to some embodiments. According to someembodiments, process 500 may include one or more of operations 501-505which may be implemented, at least in part, in the form of executablecode stored on a non-transitory, tangible, machine readable media that,when run on one or more processors, may cause a system to perform one ormore of the operations 501-505. In some examples, process 500 may beimplemented as part of operation 306 of FIG. 3.

At operation 501, the system may receive a request to provide anoptimized incentive for a product and/or service. The request may bereceived with certain data associated with the user, user device, and/oraccount that would be receiving the incentive. For example, the systemmay receive geographical information; number of times the user, userdevice, and/or account viewed the product and/or service; how long theuser, user device, and/or account has been viewing the product and/orservice; demographic information about the user; time of day; purchasehistory of the user and/or account; the type of device that is going toreceive the incentive; sensor information, such as the sensorinformation discussed in FIG. 3; and/or the like. In some examples, thesystem may receive facial images of the user or analysis of a facialimage associated with the user indicating an emotion.

At operation 502, the system may use the information received atoperation 501 to determine an optimal discount that will result in apurchase. In some examples, the system may use a neural network topredict the optimal discount. For example, the inputs for the neuralnetwork may be from one or more of the sensor information and a discountamount. In some examples, the sensor information may be normalizedand/or sampled before being used as inputs into the neural network. Theneural network may be configured to provide a binary response predictingwhether the inputs will result in a purchase.

In some examples, the system may iteratively increase the discount ofthe product and/or service as the input into the neural network until aresponse from the neural network predicts that a purchase will result.The system may also initiate the discount based on previous discounts,such as an average discount value that resulted in a purchase, or basedon a predetermined discount value. The system may also have apredetermined threshold max discount that can be provided. In someexamples, a merchant provider may provide the max discount. In thismanner, a merchant provider can ensure that the products and/or servicesare not sold at a loss.

In some examples, the system may be also configured to predict whetherother incentives would be successful in causing a purchase. For example,bundling a related item for free or at a discount. The system may assigna value to the non-monetary incentive such that the same neural networkmay be used. In some examples, the value of the free product may be usedto predict whether a purchase is made. In some examples, the system maydevalue the product by a certain amount, as individuals usually wouldvalue monetary discounts higher than a bundled item.

In some examples, the monetary discounts may be compared with bundleditem discounts to figure out how much more or less the bundled item isvalued compared to a monetary incentive. For example, the system, afterhaving provided several incentives, may determine that a bundled item onaverage is 20% higher in value than a monetary incentive to achievesimilar successful purchases numbers. The system may then discount thevalue of a bundled item by 20% when used as an input for predictingwhether the incentive will be successful in causing a purchase.

At operation 503, the system may provide the incentive. The incentivemay be the optimized discount that resulted in a predicted purchase atoperation 502. In some examples, the incentive may be the maximumdiscount, such as when the system reaches the maximum discount beforefinding a discount value that resulted in a predicted purchase atoperation 502.

At operation 504, the system may verify whether a purchase occurred. Insome examples, the discounts may have a time limit, such that the systemmay be able to verify whether the prediction was correct or not. Whenthe time limit has passed without the system receiving a verification ofthe purchase, the system can classify this prediction as incorrect. Incontrast, the system may receive an indication that a purchase was madeusing the provided discount, and classify such a prediction as correct.

In some examples, the verification of the discounted purchase may bereceived from a merchant system or another server that handles thepurchase of the product and/or services. In some examples, theverification may be received from the user device.

In some examples, the system may also receive information on how long ittook a user to accept an incentive. For example, a system may receive anindication, such as a time stamp, as to when the inventive was providedand when the product was purchased. The system may then determine basedon the time difference how successful the incentive was, and adjustaccordingly. For example, the incentive may have a time limit, and theclose the purchase was made at the beginning of the time limit, thesystem may determine that the incentive was too big. On the other hand,if the purchase was made around the middle or end of the time limit, thesystem may determine that the incentive was optimized well.

At operation 505, if the system prediction was incorrect, the system mayupdate the neural network to make better predictions in the future.Similarly, if the system determines that the incentive was too big, thesystem may again update the neural network to make a better predictionin the future. For example, the system may use some form of gradientdecent and/or back propagation on the neural network to train the neuralnetwork and update weight values.

FIG. 6 is an exemplary user device 600 that may be configured to providesensor information to one or more of the systems and/or processesdescribed above in FIGS. 3-5. Device 600 may be used as one or more ofthe client devices 104 of FIG. 1. In some examples, device 600 may be acomputer system as described in FIG. 2. Device 600 may be a device thatis in communication with the systems and/or processes described in FIGS.3-5 and provide sensor data and device information to such systems.

Device 600 may include one or more sensors, such as an image sensor 610,for sensing an object 620 in front of device 600. In some examples,image sensor 610 may include a wide angle lens. The wide angle lens mayallow for the image sensor to have a wide field of view as indicated bythe field of view lines 611 and 612. In this manner, image sensor 610may be able to detect object 620 even when close to device 600 but atthe edge of the field of view.

In some examples, sensor 610 may be one of several sensors (not shown)that are used for detecting object 620. Device 600 may have a wirelesscommunication interface which may be used to communicate the sensor datato one or more systems described above.

FIG. 7 depicts a sample tap proximity situation that one or more systemsdiscussed above may handle. As shown in FIG. 7, a user may be browsing apurchase page of an application and/or website on user device 700through touchscreen 710. The user may be interested in purchasing one ormore products being displayed on touchscreen 710, such as product 721and/or 731.

As the user is deciding whether to purchase product 721 and/or 731, theuser may hover or bring a finger 720 over and/or close to touchscreen710 in an area displaying product 721. User device 700 may have a sensor711 that may be monitoring and sensing information regarding objectsnear user device 700. Sensor 711 may be an image capturing sensor and/ora proximity sensor. Finger 720 may come cross a threshold distance thatcauses sensor 711 to sense Finger 720. User device 700 may send thesensor information to a system, such as the system in FIG. 3, where thesensor information is received at operation 301. The system may, basedon the sensor information received, determine that Finger 720 crossed athreshold distance close to the touchscreen 710 and/or determine thatFinger 720 has characteristics indicating that Finger 720 is about totap touchscreen 710. This sensor information may allow the systemdescribed in FIG. 3 and/or FIG. 4 to determine that the user had almosttapped or brought an object near touchscreen 710 where product 721 isdisplayed, as discussed in processes 303-304 of FIG. 3.

The user, however, may make a tap 730 on touch screen 710 for product731 instead of product 721. As such, the system may determine that theuser was interested in product 721 from the received sensor information,but did not make a purchase of that product. This information may bedetermined as discussed above in FIG. 3 processes 303-304.

The system, having determined that the user was interested in product721, may determine one or more actions to conduct. For example, thesystem may provide a targeted advertisement and/or incentive to the useras discussed in operations 303-306 of FIG. 3.

In some examples, the system may determine what incentive would beoptimized to cause the user to purchase product 721. For example, thesystem may use one or more operations of FIG. 5 discussed above fordetermining an incentive.

The foregoing disclosure is not intended to limit the present disclosureto the precise forms or particular fields of use disclosed. As such, itis contemplated that various alternate embodiments and/or modificationsto the present disclosure, whether explicitly described or impliedherein, are possible in light of the disclosure. For example, the aboveembodiments have focused on merchants and customers; however, a customeror consumer can pay, or otherwise interact with any type of recipient,including charities and individuals. The payment does not have toinvolve a purchase, but may be a loan, a charitable contribution, agift, etc. Thus, merchant as used herein can also include charities,individuals, and any other entity or person receiving a payment from acustomer. Having thus described embodiments of the present disclosure,persons of ordinary skill in the art will recognize that changes may bemade in form and detail without departing from the scope of the presentdisclosure. Thus, the present disclosure is limited only by the claims.

What is claimed is:
 1. A system, comprising: a non-transitory memorystoring instructions; and one or more processors coupled to thenon-transitory memory and configured to read the instructions from thenon-transitory memory to cause the system to perform operationscomprising: receiving, from a user device comprising a touchscreen,sensor information; determining, from the sensor information, that anobject is within a threshold distance of the touchscreen; in response todetermining that the object is within the threshold distance of thetouchscreen, determining, based at least on the object, content toprovide to a user associated with the user device; and communicating thecontent to the user through the user device.
 2. The system of claim 1,wherein the sensor information comprises images from a front facingcamera of the user device.
 3. The system of claim 1, wherein the contentis an incentive for a product displayed on the touchscreen.
 4. Thesystem of claim 3, wherein the operations further comprise adjusting theincentive based on a length of time the product was displayed on thetouchscreen.
 5. The system of claim 3, wherein the operations furthercomprise adjusting the incentive based on a number of times the productwas displayed on the user device.
 6. The system of claim 3, wherein theincentive is based on a received geolocation of the user device.
 7. Acomputer implemented method comprising: receiving first sensorinformation and an indication that a touchscreen was actuated on a userdevice; in response to the indication, recording the first sensorinformation in a training database; receiving second sensor informationfrom the user device; categorizing the second sensor information basedon the training database; and determining a content being displayed atthe same time as a time stamp of the second sensor information inresponse to the categorization.
 8. The method of claim 7, furthercomprising providing an incentive to a user based on the categorizationof the second sensor information.
 9. The method of claim 8, wherein theincentive is based on the content displayed on the user device.
 10. Themethod of claim 7, further comprising contacting a merchant based on thecategorization of the second sensor information.
 11. The method of claim7, further comprising causing actuation of a front facing camera of theuser device based on the categorization of the second sensorinformation.
 12. The method of claim 9, wherein the content displayed onthe user device is part of a checkout page.
 13. The method of claim 9,further comprising calculating an optimal price point for the incentivebased at least in part on the categorization of the second sensorinformation.
 14. The method of claim 13, wherein calculating the optimalprice point is based at least in part on a current price point displayedon the user device.
 15. A non-transitory machine readable medium havingstored thereon machine readable instructions executable to cause amachine to perform operations comprising: receiving sensor informationand account information from a device; determining, from the sensorinformation, that an object is within a threshold distance of atouchscreen of the device; determining a product displayed on thedevice; and displaying an discount for the product on the device basedon the determination that the object is within a threshold distance ofthe touchscreen of the device and the account information.
 16. Thenon-transitory readable medium of claim 15, wherein the operationsfurther comprise determining that a purchase occurred with the discountand adjusting a future discount based at least in part on the purchase.17. The non-transitory readable medium of claim 15, wherein theoperations further comprise determining that a purchase did not occurwith the discount within a predetermined amount of time and determininga future discount based at least in part on determining that thepurchase did not occur.
 18. The non-transitory readable medium of claim16, wherein determining the future discount based at least in part onthe purchase comprises: classifying the discount in a first category;storing the discount in a database of a plurality discounts; anddetermining a mean value from the database of the plurality of discountsbetween the first and a second category.
 19. The non-transitory readablemedium of claim 15, wherein displaying the discount comprises:determining that an account associated with the account information hasviewed the product over a threshold number of time.
 20. Thenon-transitory readable medium of claim 15, wherein an amount for thediscount is based on a geographic location associated with the accountinformation.